Since their inception, Vision Transformers (ViTs) have emerged as a
compelling alternative to Convolutional Neural Networks (CNNs) across a wide
spectrum of tasks. ViTs exhibit notable characteristics, including global
attention, resilience against occlusions, and adaptability to distribution
shifts. One underexplored aspect of ViTs is their potential for multi-attribute
learning, referring to their ability to simultaneously grasp multiple
attribute-related tasks. In this paper, we delve into the multi-attribute
learning capability of ViTs, presenting a straightforward yet effective
strategy for training various attributes through a single ViT network as
distinct tasks. We assess the resilience of multi-attribute ViTs against
adversarial attacks and compare their performance against ViTs designed for
single attributes. Moreover, we further evaluate the robustness of
multi-attribute ViTs against a recent transformer based attack called
Patch-Fool. Our empirical findings on the CelebA dataset provide validation for
our assertion.

通过研究广泛任务中视觉变换器（ViTs）与卷积神经网络（CNNs）的对比，探讨了 ViTs 的多属性学习能力，并提出了通过单个 ViT 网络训练多个属性的简单而有效的策略。通过对 CelebA 数据集的实证研究，验证了多属性 ViTs 在对抗攻击和 Patch-Fool 等最新变换器攻击下的鲁棒性。

多属性视觉转换器的高效稳健学习

Multi-Attribute Vision Transformers are Efficient and Robust Learners

Objects are usually associated with multiple attributes, and these attributes
often exhibit high correlations. Modeling complex relationships between
attributes poses a great challenge for multi-attribute learning. This paper
proposes a simple yet generic framework named Label2Label to exploit the
complex attribute correlations. Label2Label is the first attempt for
multi-attribute prediction from the perspective of language modeling.
Specifically, it treats each attribute label as a "word" describing the sample.
As each sample is annotated with multiple attribute labels, these "words" will
naturally form an unordered but meaningful "sentence", which depicts the
semantic information of the corresponding sample. Inspired by the remarkable
success of pre-training language models in NLP, Label2Label introduces an
image-conditioned masked language model, which randomly masks some of the
"word" tokens from the label "sentence" and aims to recover them based on the
masked "sentence" and the context conveyed by image features. Our intuition is
that the instance-wise attribute relations are well grasped if the neural net
can infer the missing attributes based on the context and the remaining
attribute hints. Label2Label is conceptually simple and empirically powerful.
Without incorporating task-specific prior knowledge and highly specialized
network designs, our approach achieves state-of-the-art results on three
different multi-attribute learning tasks, compared to highly customized
domain-specific methods. Code is available at
this https URL

该论文提出了一种名为 Label2Label 的简单而通用的框架，用于利用分类属性之间的复杂相关性，在多属性学习方面取得了出色的成果。